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All Projects → ndrplz → Dilation Tensorflow

ndrplz / Dilation Tensorflow

Licence: mit
A native Tensorflow implementation of semantic segmentation according to Multi-Scale Context Aggregation by Dilated Convolutions (2016). Optionally uses the pretrained weights by the authors.

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python
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Labels

deep-learningtensorflowsegmentationsemantic-segmentation

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dilation-tensorflow

A native Tensorflow implementation of semantic segmentation according to Multi-Scale Context Aggregation by Dilated Convolutions by Yu and Koltun.

Pretrained weights have been converted to TensorFlow from the original Caffe implementation.

Model pretrained either on CityScapes or on CamVid datasets is available.

You you're looking instead for a Keras+Theano implementation of this very same network you can find it here.

Examples

Cityscapes

input
Test image (input)

segmentation
Test image (prediction)

CamVid

input
Test image (input)

segmentation
Test image (prediction)

How-to

  1. Download pretrained weights from here:

    CityScapes weights

    CamVid weights

  2. Move weights file into data directory.

  3. Run the model on the test image by executing main_tf.py.

Configuration

This model has been tested with the following configuration:

  • Ubuntu 16.04
  • python 3.5.2
  • tensorflow 1.1.0
  • cv2 3.2.0

Acknowledgements

Thanks to DavideA which converted all weights from Caffe to Keras+Theano (here) making my effort of conversion towards TensorFlow much less painful than it could have been :-)

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